1. Field of Invention
This disclosure relates generally to computerized object recognition and, more particularly, to object recognition and classification using a three-dimensional (3D) system.
2. Discussion of the Background
Computerized object recognition is the process of finding or identifying an object in an image or video. Recognizing an object can include the process of classifying objects belonging to distinct classes. Object classifying using computer vision can be applied to, among other things, automated production processes, security, and automotive applications.
Typical current technology for object recognition uses images from a camera or another suitable sensor. Each image serves as an input to an object recognition algorithm, such as a neural network or another machine learning system. The image is usually fed into the algorithm as a collection of features, e.g., pixel intensities. The temporal order of such features is meaningless in the context of the single image. More importantly, the number of features can be very large, making the task of object recognition computationally very demanding.
Object recognition is known to be especially difficult if the object position and orientation is not constrained (i.e., the object may appear from an arbitrary viewing angle). In order to recognize and classify objects with a high degree of reliability, computer vision systems need to account for this variance. Also, reliable rotational invariant recognition of objects has remained an unsolved problem.
U.S. application Ser. No. 12/252,080, filed on Oct. 15, 2008, describes a system and method for object recognition and classification using a 3D system with adaptive feature detectors, which addresses aspects of the rotational invariant recognition of objects. However, the system and method described therein, in some aspects, is computationally demanding and can benefit from more accurate recognition of objects. U.S. Ser. No. 12/252,080 is incorporated herein in its entirety by reference.